Papers › CoMER: Modeling Coverage for Transformer-based Handwritten Mathematical Expression Recognition
CoMER: Modeling Coverage for Transformer-based Handwritten Mathematical Expression Recognition
Wenqi Zhao, Liangcai Gao
The Transformer-based encoder-decoder architecture has recently made significant advances in recognizing handwritten mathematical expressions. However, the transformer model still suffers from the lack of coverage problem, making its expression recognition rate (ExpRate) inferior to its RNN counterpart. Coverage information, which records the alignment information of the past steps, has proven effective in the RNN models. In this paper, we propose CoMER, a model that adopts the coverage information in the transformer decoder. Specifically, we propose a novel Attention Refinement Module (ARM) to refine the attention weights with past alignment information without hurting its parallelism. Furthermore, we take coverage information to the extreme by proposing self-coverage and cross-coverage, which utilize the past alignment information from the current and previous layers. Experiments show that CoMER improves the ExpRate by 0.61%/2.09%/1.59% compared to the current state-of-the-art model, and reaches 59.33%/59.81%/62.97% on the CROHME 2014/2016/2019 test sets.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Handwritten Mathmatical Expression Recognition | CROHME 2014 | CoMER | ExpRate | 58.38 | #7 of 17 | Archive leaderboard | report |
| Handwritten Mathmatical Expression Recognition | CROHME 2016 | CoMER | ExpRate | 56.98 | #6 of 16 | Archive leaderboard | report |
| Handwritten Mathmatical Expression Recognition | CROHME 2019 | CoMER | ExpRate | 59.12 | #6 of 14 | Archive leaderboard | report |
| Handwritten Mathmatical Expression Recognition | HME100K | CoMER | ExpRate | 68.12 | #5 of 12 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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